(Note that this is an essay I wrote when I taught at UC Berkeley; for my private tutoring business, see Solera Individualized Learning.)
Efficiently delivering individualized teaching to students with widely varying backgrounds
Students bring a variety of skill levels, knowledge, and learning styles to any course, an issue which becomes particularly apparent to me when teaching data analysis and programming. At Berkeley I have contended with some students who have a great deal of programming experience and others who have been avoiding math since high school. I have seen similar variation in writing skills as well. The challenge is how to help more remedial students while not wasting the time of more advanced students, and while also using my own time effectively. Here I share three solutions for delivering individualized teaching that have worked well for me and for my students.
My three solutions depend on the configuration of the course and vary in their scalability. They can be used together or separately and can be flexibly adapted for courses with writing assignments, lab work, and individual research projects. The first two strategies take better advantage of existing course components, while the last involves introducing a new course component.
- Make good use of office hours. Motivate students to come see you by giving them a positive incentive for doing so (e.g. extra credit) and have a sign-up process (paper or electronic). This is only scalable up to a certain number of students, however; you can only have so many office hours. This strategy is useful in many different courses, and works well with individual student projects.
- In courses with a lab component, during section, actively and systematically check in on each group of students. Ask leading questions to assess understanding and tailor these questions to each student’s level. Circulate evenly among students and bring up common issues to the whole group, e.g. by writing on the board. This does not require extra GSI preparation, but can only serve so many students in one class period. I have used this strategy in an experimental design lab course.
- For greatest flexibility in large classes, split students into groups and conduct workshops that are appropriate for the confidence level, knowledge, and skills of different subsets of the class. Survey students and discuss their needs with them directly to determine their starting point, and design workshops at different levels based on these assessments. After attending a remedial workshop, some students may be ready for more advanced ones, so offering a sequence may be useful (and more advanced workshop materials may be re-used as students progress). Workshops with interactive components are particularly helpful for students who need help with the basics. These workshops can be conducted during lecture or lab time as needed, and students can be split into as many groups as you have time to do workshops. This is the most flexible and scalable of the three methods. I have used this strategy to teach data analysis and writing skills for the Environmental Studies senior thesis course. Students in this course vary in analytical background as well as in stages of their projects; I re-used workshop materials frequently as different groups became ready for more advanced techniques. This strategy worked well in combination with (1).
I have had great success with all three strategies. In all cases, I have used survey tools in bSpace or Google Docs to create mid-semester evaluations, asking specific questions about these methods (which standardized end-of-semester evaluations never ask). The results have been overwhelmingly positive. For strategy (1), students had positive responses regarding office hours, for example, “I’ve gotten a LOT out of office hours, so I don’t know what I would change.” On strategy (2), one student commented that I was “great at tailoring teaching to the needs of the individual students.” Most students found the lab sections helpful, and also felt that they usually got enough individualized help. And regarding strategy (3), many students reported in the surveys that their knowledge and confidence had increased on a number of data analysis tasks. Nearly all the students rated the level of information in the workshop to be “just about right” and the topics presented to be “reasonably useful.” Now that the courses are over, I’ve continued to hear positive feedback on these methods from former students.
Individual attention is a casualty of large class sizes, but hopefully these examples of how to serve students at different levels will help GSIs to give more individualized help without taking on an inordinate amount of extra work.